## Artificial Intelligence

Our algorithms are the key to predicting equipment longevity for you and your customers.

 [back](https://www.digica.com/index.php?Itemid=418)

## What we do

### Failures are expensive, so we’ve developed preventive maintenance algorithms.

Failures are expensive, so we’ve developed preventive maintenance algorithms that help you look ahead by detecting potential vulnerabilities in large-scale systems and processes, and they can track anomalies that might point to future problems.

Algorithms of this type can estimate the so-called “remaining useful life” of products, assessing how long a machine is likely to run before it needs repair. Such intelligent estimates can bring substantial savings to any business.

Digica has extensive machine learning experience, from standard statistical algorithms to sophisticated [deep learning solutions](https://digica.com/), according to what the project needs. They enable the system to flag up anomalies and assess the remaining life of the product, as well as find reasons for the failure and point to possible solutions.

 ![Preventive maintenance](https://www.digica.com/images/Artificial-Intelligence/preventive-maintenance/Preventive_maintenance.jpg)

### Case studies

 ![Preventive maintenance of telecom end-user devices](https://www.digica.com/images/Artificial-Intelligence/preventive-maintenance/preventive-2.png)### Preventive maintenance of telecom end-user devices

#### What the customer wanted to achieve

The customer has a large scale telecommunications infrastructure and various end-user devices like laptops, mobile phones, tablets, STBs, and intelligent home appliances connected to end routers. Certain network conditions and end-users’ devices were causing communication breakdowns. The customer wanted to be able to monitor this complex network to avoid problems for its customers.

#### How Digica helped the customer

We trained a neural network model to predict when a specific mobile device will crash in the near future. We trained it using a continuous stream of information, unique to every mobile device, consisting of:

- Its internal state (OS, make, model, set of installed applications), and its user's behaviour (number and time of phone calls, number and time of text messages, WiFi on/off, etc.)
- Additionally, the model was able to advise customer services on preventative maintenance actions such as upgrade/downgrade of OS, removal of applications etc.

#### What we have achieved

The model predicts mobile device failure with 91% accuracy.

#### Technologies used

SHAP, decision trees (XGBoost)

- Hits: 3088

     ## Artificial Intelligence

We can enhance data from one area (like the visible spectrum) by adding information collected from another one (like the near-infrared).

 [back](https://www.digica.com/index.php?Itemid=418)

## What we do

### The Sum is Greater Than the Parts—Enhancing Images with Sensor Fusion.

Deep learning is one of many machine learning techniques that have brought the field of [computer vision](https://www.digica.com/artificial-intelligence/computer-vision.html) on in leaps and bounds. The technology has made it possible to combine different types of input data to significantly enhance an image, a technique known as sensor fusion.

We can use it to do things like add elements from the near-infrared to RGB images or blend outputs from close range radar with those from thermal sensors.

 ![Sensor fusion](https://www.digica.com/images/Artificial-Intelligence/sensor-fusion/sensor-fusion.jpg)

### Case studies

 ![Development of the process to train CNN by synthetic images](https://www.digica.com/images/Artificial-Intelligence/sensor-fusion/nearir-2.png)### Sensor fusion for RGB + near IR

#### What did Digica want to achieve

Images taken in a low-light environment have problems with:

- high noise level
- low contrast
- poor visibility

While it’s possible to improve image quality, the drawbacks are huge. Upping the exposure time introduces motion blur, which you can only get around by stabilising the camera but this only works for stationary objects. We could use flash to illuminate the subject but this isn’t always appropriate for every situation.

We wanted to find a way around these limitations, so we conducted a research project to explore methods of brightening up low-light mobile images without extending the exposure time or resorting to traditional flash.

#### How Digica met this challenge

Modern smartphones have very advanced cameras with sophisticated sensors that are sensitive to near-infrared. It’s a waveband that humans can’t see, but it holds a lot of valuable information that can help us see better in dark conditions. Pictures become much brighter after we merge colour information from the visible spectrum with shapes from the near-infrared. We did use a flash in the end, but not a normal one. It lights up the subject in the near-infrared only, so while your eyes don’t notice it, the final image certainly does.

#### What we achieved

Compared to traditional methods PSNR (peak signal-to-noise ratio) grew from 8.12 to 25.51 and the SSIM (structural similarity index) grew from 0.044 to 0.833.

#### Technologies used

Sony IMX219 sensor, Near-Infrared LED flash 850nm, TensorFlow

- Hits: 2953

     ## Artificial Intelligence

AI lets you easily find and classify objects wherever you may be looking. Whether that’s in the realm of thermal images, spectrometry, micro-doppler spectra from radar, or sound.

 [back](https://www.digica.com/index.php?Itemid=418)

## What we do

### [Deep Learning in Computer Vision](https://www.digica.com/artificial-intelligence/computer-vision.html)

Deep Learning has made Computer Vision techniques viable in many more areas than before. There was already a good deal of industry knowledge and experience around detecting and classifying objects in the visible portion of the electromagnetic spectrum, but not so much in other areas.

Digica has developed expertise that opens up new domains like the InfraRed and near-InfraRed, Ion-Mobility spectrometry, micro-Doppler signals from FMCW radar, and the audible spectrum.

 ![Deep Learning in Computer Vision](https://www.digica.com/images/Artificial-Intelligence/advanced-domains/advanced-domains.jpg)

### Case studies

- ![Objects detection with FMCW radar](https://www.digica.com/images/Artificial-Intelligence/advanced-domains/doppler.png)### Objects detection with FMCW radar

    #### What the customer wanted to achieve

    FMCW radar is good at detecting intrusions into the airspace of busy airports, but not so good at differentiating between objects of a similar size like drones and birds. Since birds will always be there and drones should only be there by invitation, the customer needed to be able to tell the difference between them, fast. False alarms can trigger crippling disruption to operations, so they wanted a system that would be able to distinguish between birds and drones reliably.

    #### How Digica helped the customer

    The team developed a unique way of processing domain-specific signals in FMCW radar data to train convolutional neural networks (CNNs) to successfully classify objects.

    #### What we achieved

    ##### Scalar data (azimuth, distance, speed, etc.), 1s observation

    gain in Recall: 5%-10%

    ##### Scalar data + microDoppler spectra, 1s observation

    gain in Recall: 20%-25%

    ##### Scalar data + microDoppler spectra, 5s observation

    gain in Recall: 30%

    #### Technologies used

    Convolution Neural Networks, TensorFlow, SVM, scikit-learn, scikit-image
- ![Detecting chemical compounds](https://www.digica.com/images/Artificial-Intelligence/advanced-domains/chemical.png)### Detecting chemical compounds

    #### What the customer wanted to achieve

    Ion mobility spectrometry is used for detecting dangerous substances, but it only works when you can shield the data from the adverse effects of noise and different conditions. This usually takes a lot of analysis and a lot of time, both of which are costly, so the customer wanted quicker analysis results that were at least as accurate as traditional methods.

    #### How Digica helped the customer

    We developed CNN models that can detect predetermined sets of chemical compounds in ion mobility spectrometry data. Despite unwanted noise and measurement fluctuations that can occur in different ambient conditions, our method is still able to identify which parts of the measured spectrum are the most important for classification.

    #### What we achieved

    Digica identified a subset of features which amount to just 25% of the original information, so prediction speed was increased to 400% with no loss of accuracy.

    #### Technologies used

    TensorFlow, Keras, CNNs

- ![Sound-based tires classification](https://www.digica.com/images/Artificial-Intelligence/advanced-domains/waveform1.png)### Sound-based tires classification

    #### What the customer wanted to achieve

    A major tire manufacturer wanted an efficient way to detect when truck tyres need maintenance. The system would use gate-mounted microphones to capture the sound of passing vehicles.

    #### How Digica helped the customer

    Our objective was to take the captured audio samples of tyres on the move and match them with established classes of tires in different states, such as normal, under-inflated, small object embedded, and so on. Digica developed a custom convolution neural network that learnt how to accurately distinguish between tyres in these different states.

    #### What we achieved

    The system classifies tyres with 93% accuracy.

    #### Technologies used

    Convolution Neural Networks (Keras), specially designed FFT filters (numpy), Python
- ![Heart murmurs detection](https://www.digica.com/images/Artificial-Intelligence/advanced-domains/heart.png)### Heart murmurs detection

    #### What the customer wanted to achieve

    The customer wanted to overcome the challenges involved with recognising heartbeat characteristics. The sound of a heart changes when you listen with different kinds of stethoscopes and factors like the patient’s age and aspects of their general health can also modify the sound in unhelpful ways too.

    #### How Digica helped the customer

    We developed a neural network that can detect potentially problematic heart murmurs, despite all the factors that can obscure this information in the audio.

    #### What we achieved

    The system successfully identified heartbeat characteristics in 92% of recordings.

- Hits: 3039

     ## Artificial Intelligence

This is [Artificial Intelligence](https://www.digica.com/artificial-intelligence.html) working directly with human – on devices that we have at hour homes, offices, even on our clothes. Encapsulating “silicon brain” so close to us, allow for direct, immediate use of the newest method, so needed in medicine, autonomous driving, surveillance, and plenty of other fields.

 [back](https://www.digica.com/index.php?Itemid=418)

## What we do

### Many different types of Edge devices

We work with many different types of Edge devices. From typical mighty Linux-based small boxes like Raspberry Pi, through our clients’ propriety equipment, to even small accessories like GPU-enabled RGB and thermal cameras.

Our devices work under a variety of circumstances – from big, multiprocessors servers spotting threats around high-value areas, to small, low powered IoT devices where every microWatt and every CPU tic counts and costs. Depending on the situation, we deploy either complex, comprehensive models with robust functionalities, or very small, one-function only algorithms.

When it comes to Edge Computing, flexibility is the X factor.

 ![](https://www.digica.com/images/Artificial-Intelligence/edge-computing/Edge_Computing.jpg)

### Case studies

- ![Classification of small electronic devices  ](https://www.digica.com/images/Artificial-Intelligence/edge-computing/electronics.png)### Classification of small electronic devices

    #### What the customer wanted to achieve

    One of the biggest distributors of electronics in the world asked Digica to create a solution for detecting and classifying all 300,000 products in a mobile application.

    It’s best to train a machine learning model with a lot of data, but here there were only 15 pictures available for each product, and they were taken by professionals. That might sound like an advantage, but in this case, a studio environment with perfect lighting conditions made them very different from the kind of pictures taken by the end customers with their mobile devices.

    #### How Digica helped the customer

    We used a loss function called Triplet Loss. It’s effective when you only have a limited amount of data to work with, and we combined it with several computer vision techniques to introduce variety to the initial “too perfect” dataset.

    #### What we achieved

    Classification of main categories: **97%**

    Classification of subcategories: **92%**

    Classification of a SKU: **87%**

    #### Technologies used

    Neural Networks, Keras, TensorFlow, Triplet Loss, VGG16, Computer Vision techniques
- ![Detection and classification of any LEGO elements](https://www.digica.com/images/Artificial-Intelligence/edge-computing/lego.png)### Detection and classification of any LEGO elements

    #### What the customer wanted to achieve

    The customer wanted to provide a great real-time experience for detecting and classifying various LEGO pieces in a scene filmed on a mid-performance mobile device. This presented us with three serious challenges:

    - A lack of pictures of real LEGO pieces taken under different lighting conditions, showing genuine children’s play situations.
    - Some of the LEGO pieces weren’t available because they were still at the design stage, and they wouldn’t be available for months.
    - The mobile devices used were underpowered. They simply didn’t have enough processing “grunt” to run sophisticated machine learning models with enough speed and accuracy.

    #### How Digica helped the customer

    We created a new type of deep neural network that can recognise and classify any LEGO piece out of 400,000 possible choices. We designed it to train on synthetic images rather than real-world too, which has probably saved us a thousand years’ worth of studio time photographing Lego blocks in mocked-up bedrooms.

    #### What we achieved

    mAP 89%, real time detection and classification experience

    #### Technologies used

    TensorFlow, TensorFlow Lite, CoreML, Unity 3D, our proprietary “Synthesis” method

- Hits: 3142

     ## Artificial Intelligence

The progress vision-based technologies achieved by the use of [Artificial Intelligence](https://www.digica.com/artificial-intelligence.html) is nothing short of a miracle. We can now detect fires, recognise people by their faces and spot cancer on tissue samples. With speed and precision beyond human capabilities.

 [back](https://www.digica.com/index.php?Itemid=418)

## What we do

### Newest Computer Vision methods

At Digica, we use the newest Computer Vision methods, in conjunction with Artificial Intelligence to any data and problems that can be translated into images or movies. We detect different objects (people, vehicles, drones, etc.) both in visible light, in thermal, and near-infrared spectra.

We work with radar data, where we use radio frequencies electromagnetic fields to build images of landscapes and weather, but also to detect different objects from far away or from a very close distance as we do in gesture recognition projects.

We use 2d and 3d imaging for chemical projects, where we analyze signals from spectrometers and other chemical devices.

 ![](https://www.digica.com/images/Artificial-Intelligence/computer-vision/Vision_methods.jpg)

### Case studies

- ![Development of the process to train CNN by synthetic images](https://www.digica.com/images/Artificial-Intelligence/computer-vision/cnn.png)### Development of the process to train CNN by synthetic images

    #### What the customer wanted to achieve

    The customer wanted a system that can detect objects like vehicles, people, and animals in different situations such as in the street, the sky, or underwater. We couldn’t use the usual tens or even hundreds of thousands of pictures to train the system, which was an extra challenge.

    #### How Digica helped the customer

    We developed a unique method of generating and training convolutional neural network models based on synthetic images. For the overall process:

    - we developed and prepared the environment, basing it on the Caffe deep learning framework
    - developed steering scripts to simulate the natural environment using Unity 3D
    - developed variants of the image object classifiers
    - modified network hyperparameters to improve detection precision
    - transformed synthetic images to improve results

    #### What we achieved

    Created a repeatable process for building neural networks based on synthetic images.

    #### Technologies used

    Caffe, Unity 3D, Python, Keras, TensorFlow
- ![Detecting people in masks on thermal images during COVID crisis](https://www.digica.com/images/Artificial-Intelligence/computer-vision/mask.png)### Detecting people in masks on thermal images during COVID crisis

    #### What the customer wanted to achieve

    Facial recognition technology has come a long way, but during the COVID-19 crisis systems with infrared cameras were failing to detect many people.

    The customer wanted to help during the COVID-19 pandemic with a temperature scanning station that brought together infrared cameras with facial recognition technology. The tricky part was that surgical masks being worn to limit the spread of infection were confusing to existing systems.

    #### How Digica helped the customer

    We used RGB pictures of people in masks and thermal images that we created in-house using an adaptation technique. Once the detector had been trained using these image sets it was able to identify people wearing masks and send their location to the temperature measuring algorithm. We could describe the result as an ‘automated remote thermometer’ and it helped scanning stations to prevent the spread of COVID-19.

    #### What we achieved

    Recall increased from 90% to over 99%.

    #### Technologies used

    TensorFlow, MobileNet, SSD

- ![Road signs detection](https://www.digica.com/images/Artificial-Intelligence/computer-vision/readsigns.png)### Road signs detection

    #### What the customer wanted to achieve

    The customer wanted accurate road sign detection for autonomous vehicles that would still run on devices with low computing power.

    #### How Digica helped the customer

    Road-mapping cars receive a vast quantity of visual data every second. This means that efficiency is key in the processing and analysis of that data. Using neural networks our model was able to immediately recognise and segment road signs and markings. These processes are essential for autonomous car’s driving systems, as the precise interpretation of road markings is critical to their successful operation.

    #### What we achieved

    The system recognised road signs with 90% accuracy while preserving set Jaccard Index parameters.

    #### Technologies used

    Python, Keras, TensorFlow on GPU.

    SSD Algorithms: Single Shot MultiBox Detector
- ![Fixed pattern noise removal](https://www.digica.com/images/Artificial-Intelligence/computer-vision/noise.png)### Fixed pattern noise removal

    #### What the customer wanted to achieve

    Thermal cameras are susceptible to both external (environmental) and internal (built-in) conditions. The objective of this project was to remove the fixed pattern noise.

    The primary concern was to remove the noise whilst preserving the real image. This meant that no additional data (ghosting) should appear after noise removal. Since there are three different types of noise that affect thermal images, each one had to be removed separately.

    #### How Digica helped the customer

    Digica developed a method which was customized and adjusted for the very specific conditions that apply to the images collected by the customer.

    #### What we achieved

    Decreased low-frequency noise by 80%.

    Decreased the number of artefacts by 30%.

    Reduced high-frequency noise by 20%.

    #### Technologies used

    Keras, TensorFlow, Python

 ![Visual image processing - CNNs with transfer learning and Hinton capsules](https://www.digica.com/images/Artificial-Intelligence/computer-vision/catscan.png)### Visual image processing - CNNs with transfer learning and Hinton capsules

#### What the customer wanted to achieve

The customer wanted to be able to recognise benign tumours from a set of CAT scan images. The main challenge was the low signal data.

#### How Digica helped the customer

Digica developed a neural network to recognise benign tumours on a set of CAT scans.

We adopted a transfer learning approach to pre-train the model and apply Hinton capsules in the second stage.

#### What we achieved

CNNs – 71%

CNNs with transfer learning – 78%

Capsule Network – 84%

#### Technologies used

Convolutional neural networks, with transfer learning and capsules network

- Hits: 4135

     - ![How Technology Can Improve Life in an Aging World](https://www.digica.com/images/landing/ai-healthcare/Ebook_Digica_How_Technology_Can_Improve_Life_in_an_Aging_World.png)## How Technology Can Improve Life in an Aging World

    With a rapidly aging population, AI and digital health solutions are transforming senior care. This ebook highlights **how robotics, AI-driven diagnostics, telemedicine, and universal design** are reshaping the way care is delivered- helping professionals provide more effective, personalized support.

    **Get your copy now** and discover how technology is driving better outcomes in senior care.
- ![5 Major Causes of Failure in Medical AI Projects and How to Overcome Them](https://www.digica.com/images/landing/ai-healthcare/Ebook_Digica_5_major_causes_of_failure_in_medical_AI_projects.png)## 5 Major Causes of Failure in Medical AI Projects and How to Overcome Them

    Medical AI offers groundbreaking opportunities, but why do so many projects struggle to deliver results? This ebook explores **five key challenges in medical AI development**- from data limitations to model transparency- and provides **clear, actionable solutions** to help organizations navigate these obstacles.

    **Download the guide today** to ensure your AI-driven healthcare solutions are built for success.

# Advancing Healthcare with AI-Driven Innovation

**Enhancing Efficiency, Accuracy, and Patient Outcomes**

- **Optimized Medical Imaging** - AI-powered analysis for faster and more precise diagnostics.
- **Automated Data Processing** - Improving efficiency in patient records, diagnostics, and workflows.
- **Remote Monitoring & Predictive Analytics** - Enabling proactive care and early disease detection.
- **AI-Assisted Diagnostics** - Enhancing decision support for medical professionals.

## Download for free

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## Here are some examples of our recent work

 ![Deep Learning in detection of microscopic tissue features](https://www.digica.com/images/landing/ai-healthcare/Deep_Learning_in_detection_of_microscopic_tissue_features.png) ![Deep Learning in detection of microscopic tissue features 1](https://www.digica.com/images/landing/ai-healthcare/Deep_Learning_in_detection_of_microscopic_tissue_features-1.png) ![Deep Learning in detection of microscopic tissue features 2](https://www.digica.com/images/landing/ai-healthcare/Deep_Learning_in_detection_of_microscopic_tissue_features-2.png)

### Deep Learning in detection of microscopic tissue features

Digica was tasked with detecting features in digitised microscopic tissue images. The project was unorthodox due to the scarcity of labelled examples and the target images having resolution in the order of thousands of megapixels.

The project demanded segmentation or detection of features such as cell nuclei, artefacts and microvessels.

### Preventing cot deaths using radar

We used a small radar system to monitor a baby's breathing pattern.

For every 2000 babies born, one of them will stop breathing in the middle of the night and die. We want to prevent this by creating an automatic, remote breath control device that triggers an alarm when the baby has difficulty breathing.

 ![Preventing cot deaths using radar](https://www.digica.com/images/landing/ai-healthcare/Preventing_cot_deaths_using_radar.png) ![Preventing cot deaths using radar 1](https://www.digica.com/images/landing/ai-healthcare/Preventing_cot_deaths_using_radar-1.png) ![Preventing cot deaths using radar 2](https://www.digica.com/images/landing/ai-healthcare/Preventing_cot_deaths_using_radar-2.png)

 ![Measuring various life signs using smartphone derived facial video](https://www.digica.com/images/landing/ai-healthcare/Measuring_various_life_signs_using_smartphone-derived_facial_video.png) ![Measuring various life signs using smartphone derived facial video 1](https://www.digica.com/images/landing/ai-healthcare/Measuring_various_life_signs_using_smartphone-derived_facial_video-1.png) ![Measuring various life signs using smartphone derived facial video 2](https://www.digica.com/images/landing/ai-healthcare/Measuring_various_life_signs_using_smartphone-derived_facial_video-2.png)

### Measuring various life signs using smartphone-derived facial video

We created a cross platform library for in-vehicle and remote diagnostic human monitoring.

Using photoplethysmography (PPG) and a standard RGB camera, blood volume changes were detected to measure heart rate variability, respiration rate and blood oxygen saturation.

### Optimisation of surgical instrumentation usage

Driven by [computer vision](https://www.digica.com/artificial-intelligence/computer-vision.html) and synthetic data, a deep learning model is incorporated into a mobile application allowing medical personnel to automatically identify medical trays and their contents for use in surgery.

**Before surgery**, staff take a picture of a surgical tool tray using their smartphone and are notified if instruments are missing.

**After surgery**, staff are able to determine which instruments have been used or may be missing.

 ![Optimisation of surgical instrumentation usage](https://www.digica.com/images/landing/ai-healthcare/Optimisation_of_surgical_instrumentation_usage.png) ![Optimisation of surgical instrumentation usage 1](https://www.digica.com/images/landing/ai-healthcare/Optimisation_of_surgical_instrumentation_usage-1.png) ![Optimisation of surgical instrumentation usage 2](https://www.digica.com/images/landing/ai-healthcare/Optimisation_of_surgical_instrumentation_usage-2.png)

 ![AI Edge supports people with impaired vision 01](https://www.digica.com/images/landing/ai-healthcare/AI_Edge_supports_people_with_impaired_vision.png) ![AI Edge supports people with impaired vision 02](https://www.digica.com/images/landing/ai-healthcare/AI_Edge_supports_people_with_impaired_vision-1.png) ![AI Edge supports people with impaired vision 03](https://www.digica.com/images/landing/ai-healthcare/AI_Edge_supports_people_with_impaired_vision-2.png)

### [AI Edge](https://www.digica.com/artificial-intelligence/edge-computing.html) supports people with impaired vision

Supporting people with impaired vision by detecting nearby obstructions has been achieved by applying computer vision through a camera integrated into a walking stick.

Our AI-based object detection system recognises surfaces and moving objects based on proximity and potential hazard and communicates this via a haptic controller and Bluetooth earpiece.

The first prototype implementation is being trialled with **[Blackworld](https://blackworld.pl/en/)**, a blind and partially sighted organisation in Poland

## FAQ

### Frequently Asked Questions

 [Show all FAQs](https://www.digica.com/index.php?Itemid=997)

### "There aren’t that many people who have as good a knowledge base as the Digica team."

### Leading global imaging technology company

- Hits: 5402

     ## Artificial Intelligence

# Reveal the pattern

Our AI can detect and classify objects that lie beyond the visible spectrum, solve problems despite gaps in your data and combine data from separate sources, prevent failures before they can happen.

## What we do

### Applied Artificial Intelligence

No organisation is short of information these days, but they miss out when they can’t make sense of it all. We can help you get more from your data by supplementing standard analytics processes with machine learning and artificial intelligence techniques and methodologies.

We apply our in-house developed tools, platforms, and techniques together with additional relevant datasets to help our customers extract the hidden patterns in their data.

### Experience

**Digica** has the pedigree to offer a wide range of applied artificial Intelligence solutions to our customers thanks to the expertise that **Enigma Pattern** - formerly a separate company - brings to the partnership.

Enigma Pattern’s considerable knowledge and experience in AI and machine learning was gained on numerous research and development projects for top tier technology companies. Projects in the portfolio range from medical innovations through to object detection using radar.

Now, Digica’s AI wing in Poland continues to build on that legacy with self-guided research into cutting-edge topics. A wealth of industry experience and boundless curiosity give our team the insights they need to tackle your most pressing challenges.

### Deep learning gives

computer vision more to see
in these areas

- Ion-Mobility Spectrometry
- CAT scans
- FMCW radars
- Preventive Maintenance
- Thermal images

 ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_01.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_06.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_21.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_36.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_38.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_14.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_31.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_33.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_03.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_24.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_26.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_11.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_09.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_18.jpg) ![](https://www.digica.com/images/Artificial-Intelligence/grid/ai-grid_28.jpg)## What we do

### Areas of expertise

We have deep expertise in the field of image processing including Deep Learning for [Computer Vision](https://www.digica.com/artificial-intelligence/computer-vision.html) and leading edge commercial implementation of Synthetic Imaging.

- [Objects detection and classification in advanced domains](https://www.digica.com/artificial-intelligence/advanced-domains.html)
- [Enhancing images and sensor fusion](https://www.digica.com/artificial-intelligence/sensor-fusion.html)
- [General Computer Vision projects](https://www.digica.com/artificial-intelligence/computer-vision.html)

Our work also covers the fields of Financial Trading, Audio Analysis and Predictive Maintenance. Read more about:

- [Preventive maintenance](https://www.digica.com/artificial-intelligence/preventive-maintenance.html)
- [Edge Computing](https://www.digica.com/artificial-intelligence/edge-computing.html)
- ![Advanced domains](https://www.digica.com/images/23/svg/Advanced--domains.svg)### Advanced

    domains

    AI lets you easily find and classify objects wherever you may be looking.
- ![Sensor Fusion](https://www.digica.com/images/23/svg/Sensor-Fusion.svg)### Sensor

    Fusion

    The Sum is Greater Than the Parts—Enhancing Images with Sensor Fusion.

- ![Edge Computing](https://www.digica.com/images/23/svg/Edge-Computing.svg)### Edge

    Computing
- ![AI SDK](https://www.digica.com/images/23/svg/ico-sdk.svg)### AI

    SDK

    Designed to reduce development time for real-world use cases while enhancing overall quality.
- ![LLMs](https://www.digica.com/images/23/svg/lms2.svg)###

    LLMs

    Our algorithms are the key to predicting equipment longevity for you and your customers.
- ![Cloud Development](https://www.digica.com/images/23/svg/Cloud.svg)### Cloud

    Development
- ![Computer Vision](https://www.digica.com/images/23/svg/Computer--Vision.svg)### Computer

    Vision
- ![PreventiveMaintenance ](https://www.digica.com/images/23/svg/Preventive-Maintenance.svg)### Preventive

    Maintenance

- Hits: 4372

     # Delivering value and transforming performance in healthcare through AI

**Driving cost out of medical process**

- **Surgical equipment** - instrument tray imaging
- **Digital pathology** - microscopic tissue feature detection
- **Telemedicine** - remote life signs measurement
- **Digital stethoscope** - heart murmur detection

## Let’s meet

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### 5 major causes of failure in medical AI projects and how to overcome them

[Medical AI](https://www.digica.com/digica-delivering-value-and-transforming-performance-in-healthcare-through-ai.html) is a powerful tool that can do a lot of good. It’s also glamorous: leads to much innovation and attracts a lot of mainstream media.

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## Here are some examples of our recent work

 ![Deep Learning in detection of microscopic tissue features](https://www.digica.com/images/landing/ai-healthcare/Deep_Learning_in_detection_of_microscopic_tissue_features.png) ![Deep Learning in detection of microscopic tissue features 1](https://www.digica.com/images/landing/ai-healthcare/Deep_Learning_in_detection_of_microscopic_tissue_features-1.png) ![Deep Learning in detection of microscopic tissue features 2](https://www.digica.com/images/landing/ai-healthcare/Deep_Learning_in_detection_of_microscopic_tissue_features-2.png)

### Deep Learning in detection of microscopic tissue features

Digica was tasked with detecting features in digitised microscopic tissue images. The project was unorthodox due to the scarcity of labelled examples and the target images having resolution in the order of thousands of megapixels.

The project demanded segmentation or detection of features such as cell nuclei, artefacts and microvessels.

### Preventing cot deaths using radar

We used a small radar system to monitor a baby's breathing pattern.

For every 2000 babies born, one of them will stop breathing in the middle of the night and die. We want to prevent this by creating an automatic, remote breath control device that triggers an alarm when the baby has difficulty breathing.

 ![Preventing cot deaths using radar](https://www.digica.com/images/landing/ai-healthcare/Preventing_cot_deaths_using_radar.png) ![Preventing cot deaths using radar 1](https://www.digica.com/images/landing/ai-healthcare/Preventing_cot_deaths_using_radar-1.png) ![Preventing cot deaths using radar 2](https://www.digica.com/images/landing/ai-healthcare/Preventing_cot_deaths_using_radar-2.png)

 ![Measuring various life signs using smartphone derived facial video](https://www.digica.com/images/landing/ai-healthcare/Measuring_various_life_signs_using_smartphone-derived_facial_video.png) ![Measuring various life signs using smartphone derived facial video 1](https://www.digica.com/images/landing/ai-healthcare/Measuring_various_life_signs_using_smartphone-derived_facial_video-1.png) ![Measuring various life signs using smartphone derived facial video 2](https://www.digica.com/images/landing/ai-healthcare/Measuring_various_life_signs_using_smartphone-derived_facial_video-2.png)

### Measuring various life signs using smartphone-derived facial video

We created a cross platform library for in-vehicle and remote diagnostic human monitoring.

Using photoplethysmography (PPG) and a standard RGB camera, blood volume changes were detected to measure heart rate variability, respiration rate and blood oxygen saturation.

### Optimisation of surgical instrumentation usage

Driven by [computer vision](https://www.digica.com/artificial-intelligence/computer-vision.html) and synthetic data, a deep learning model is incorporated into a mobile application allowing medical personnel to automatically identify medical trays and their contents for use in surgery.

**Before surgery**, staff take a picture of a surgical tool tray using their smartphone and are notified if instruments are missing.

**After surgery**, staff are able to determine which instruments have been used or may be missing.

 ![Optimisation of surgical instrumentation usage](https://www.digica.com/images/landing/ai-healthcare/Optimisation_of_surgical_instrumentation_usage.png) ![Optimisation of surgical instrumentation usage 1](https://www.digica.com/images/landing/ai-healthcare/Optimisation_of_surgical_instrumentation_usage-1.png) ![Optimisation of surgical instrumentation usage 2](https://www.digica.com/images/landing/ai-healthcare/Optimisation_of_surgical_instrumentation_usage-2.png)

 ![AI Edge supports people with impaired vision 01](https://www.digica.com/images/landing/ai-healthcare/AI_Edge_supports_people_with_impaired_vision.png) ![AI Edge supports people with impaired vision 02](https://www.digica.com/images/landing/ai-healthcare/AI_Edge_supports_people_with_impaired_vision-1.png) ![AI Edge supports people with impaired vision 03](https://www.digica.com/images/landing/ai-healthcare/AI_Edge_supports_people_with_impaired_vision-2.png)

### [AI Edge](https://www.digica.com/artificial-intelligence/edge-computing.html) supports people with impaired vision

Supporting people with impaired vision by detecting nearby obstructions has been achieved by applying computer vision through a camera integrated into a walking stick.

Our AI-based object detection system recognises surfaces and moving objects based on proximity and potential hazard and communicates this via a haptic controller and Bluetooth earpiece.

The first prototype implementation is being trialled with **[Blackworld](https://blackworld.pl/en/)**, a blind and partially sighted organisation in Poland

## FAQ

### Frequently Asked Questions

 [All FAQ](https://www.digica.com/index.php?Itemid=997)

### "There aren’t that many people who have as good a knowledge base as the Digica team."

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